We propose to develop software for tree-based methods which implements research on biomedical applications not currently supported by commercial software. Examples include survival and longitudinal data analysis, clustering, and generalized tree models. Tree-based tools offer many advantages including easy interpretation. Tree representations may mimic the way that many medical scientists think about data. Trees are also relatively fast to build and search, making them suitable for interactive clustering of large data sets such as encountered in image analysis. Phase II research will develop algorithms, model selection strategies, and stability diagnostics for tree based methods. Dynamic, interactive graphical tools will enable the user to explore the resulting tree-based model, and interpret structure in data by linking the tree representation to a variety of graphical and analytical tools. We will create software that analysts find flexible and easy to use, enabling medical researchers to use tree-based tools to explore and understand data from a wide variety of applications. Additionally, the software will be supported in an integrated environment for data analysis, and permit analysts, consultants, and statistical researchers to extend the software to incorporate future innovations in recursive partitioning research. PROPOSED COMMERCIAL APPLICATIONS: This SBIR will result in a new software package which is inter-operable with existing S-Plus products. We expect this product to appeal to a wide market of users including data analysts, consultants, and researchers in disciplines as diverse as medicine, pattern recognition, data mining, and image analysis.